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Panel
titlePlot ensembles with your cluster definitions

Use the clusters of ensemble members you have created in ens_oper_cluster.example.txt.

Set clustersId='example' in each of the ensemble plotting macros to enable cluster highlighting.

Replot ensembles:

RMSE: plot the RMSE curves using ens_rmse.mv. This will colour the curves differently according to which cluster they are in.

Stamp maps: the stamp maps will be reordered such at the ensemble members will be groups according to their cluster. Applies to stamp.mv and stamp_diff.mv. This will make it easier to see the forecast scenarios according to your clustering.

Spaghetti maps: with clusters enabled, two additional maps are produced which show the contour lines for each cluster. The spaghetti maps are similar to Figure 10. in Pantillon et al.

Panel
titlePlot maps of parameters as clusters

The macro cluster_to_an.mv can be used to plot maps of parameters as clusters and compared to the analysis and HRES forecasts.

Use cluster_to_an.mv to plot z500 maps of your two clusters (equivalent to Figure 7 in Pantillon et al.)

If your cluster definition file is called 'ens_oper_cluster.example.txt', then Edit cluster_to_an.mv and set:

Code Block
languagebash
#ENS members (use ["all"] or a list of members like [1,2,3]
members_1=["cl.example.1"]
members_2=["cl.example.2"]

If your cluster definition file is has another name, e.g. ens_oper_cluster.fred.txt, then members_1=["cl.fred.1"].

Plot other parameters:

Plot total precipitation for France (mapType=2). Compare with Figure 8. in Pantillon et al.

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borderColorred

Q. Experiment with the choice of members in each clusters and plot z500 at t+96 (Figure 7 in Pantillon et al.). How similar are your cluster maps?
Q. What date/time does the impact of the different clusters become apparent?
Q. Are two clusters enough? Where do the extreme forecasts belong?

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A quantitative way of clustering an ensemble is by computing empirical orthogonal functions from the differences between the ensemble members and the control forecast.

Although geopotential height at 500hPa at 00 24/9/2012 is used in the paper by Pantillon et al., the steps described below can be used for any parameter at any step.

To use the principal component analysis (PCA), the The eof.mv macro computes the EOFs and the clustering:.

Warning

Always use the eof.mv first for a parameter and ensemble forecast (e.g. ens_oper or ens_2016) to create the cluster file. Otherwise the cluster_to_an.mv and other plots with clustering enabled will fail.

Panel
titleCompute EOFs and clusters

Edit 'eof.mv'

Set the parameter, choice of ensemble and forecast step required for the EOF computation:

Code Block
param="z500"
expId="ens_oper"
steps=[2012-09-24 00:00]

Run the macro.

The above example will compute the EOF of geopotential height anomaly at 500hPa using the 2012 operational ensemble at forecast step 00Z on 24/09/2012.

A plot will be generated appear showing the first two EOFs (similar to Figure 5 in Pantillon et al.)

This will create a text file: (TO BE DONE)

The The geographical area for the EOF computation is: 35-55N, 10W-20E (same as in Pantillon et al). If desired it can be changed in eof.mv.

Panel
titleEOF cluster definition file

The eof.mv macro will create a text file with the cluster definitions, in the same format as described above in the previous task.

The filename will be different, it will have 'eof' in the filename to indicate it was created by using empirical orthogonal functions.

Code Block
languagebash
titleCluster filename created for ensemble 'ens_oper' using eof.mv
ens_oper_cluster.eof.txt

If a different ensemble forecast is used, for example ens_2016, the filename will be: ens_2016_cluster.eof.mv

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borderColorred

Q. What do the EOFs plotted by eof.mv show?
Q. Change the parameter used for the EOF (try the 'total precipitation' field). How does the cluster change?

Panel
titlePlot cluster maps

 The cluster_to_an.mv macro will use the clustering information and

Set the parameter to that used in eof.mv

From Figure 7 in Pantillon et al. we see that cluster 1 corresponds to a cutoff low moving eastward over Europe and cluster 2 to a weak ridge over western Europe. Cluster 1 exhibits a weak interaction between Nadine and the cut-off low over Europe. In cluster 2, there is a strong interaction between the cutoff and Nadine in which Nadine makes landfall over the Iberian penisula.

 

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Exercise 5. Exploring the role of uncertainty

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